1 code implementation • 28 Nov 2023 • Théo Bourdais, Pau Batlle, Xianjin Yang, Ricardo Baptista, Nicolas Rouquette, Houman Owhadi
Type 1: Approximate an unknown function given input/output data.
2 code implementations • 25 Oct 2023 • Zheyu Oliver Wang, Ricardo Baptista, Youssef Marzouk, Lars Ruthotto, Deepanshu Verma
PCP-Map models conditional transport maps as the gradient of a partially input convex neural network (PICNN) and uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives.
1 code implementation • 12 Oct 2023 • Mathieu Le Provost, Ricardo Baptista, Jeff D. Eldredge, Youssef Marzouk
In these settings, the Kalman filter and its ensemble version - the ensemble Kalman filter (EnKF) - that have been designed under Gaussian assumptions result in degraded performance.
1 code implementation • 9 Jul 2023 • Jason Alfonso, Ricardo Baptista, Anupam Bhakta, Noam Gal, Alfin Hou, Isa Lyubimova, Daniel Pocklington, Josef Sajonz, Giulio Trigila, Ryan Tsai
Sampling conditional distributions is a fundamental task for Bayesian inference and density estimation.
no code implementations • 14 Feb 2023 • Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, Jiaming Song, Karsten Kreis, Jan Kautz, Christopher Pal, Arash Vahdat, Anima Anandkumar
They consist of a forward process that perturbs input data with Gaussian white noise and a reverse process that learns a score function to generate samples by denoising.
1 code implementation • 31 Oct 2022 • Maximilian Ramgraber, Ricardo Baptista, Dennis McLaughlin, Youssef Marzouk
Smoothing is a specialized form of Bayesian inference for state-space models that characterizes the posterior distribution of a collection of states given an associated sequence of observations.
1 code implementation • 31 Oct 2022 • Maximilian Ramgraber, Ricardo Baptista, Dennis McLaughlin, Youssef Marzouk
A companion paper (Ramgraber et al., 2023) explores the implementation of nonlinear ensemble transport smoothers in greater depth.
no code implementations • 22 Jun 2022 • Ricardo Baptista, Lianghao Cao, Joshua Chen, Omar Ghattas, Fengyi Li, Youssef M. Marzouk, J. Tinsley Oden
We tackle this challenging Bayesian inference problem using a likelihood-free approach based on measure transport together with the construction of summary statistics for the image data.
2 code implementations • 10 Mar 2022 • Mathieu Le Provost, Ricardo Baptista, Youssef Marzouk, Jeff D. Eldredge
We propose a regularization method for ensemble Kalman filtering (EnKF) with elliptic observation operators.
no code implementations • 8 Jul 2021 • Rebecca E Morrison, Ricardo Baptista, Estelle L Basor
For a multivariate normal distribution, the sparsity of the covariance and precision matrices encodes complete information about independence and conditional independence properties.
no code implementations • 8 Jan 2021 • Ricardo Baptista, Youssef Marzouk, Rebecca E. Morrison, Olivier Zahm
Undirected probabilistic graphical models represent the conditional dependencies, or Markov properties, of a collection of random variables.
1 code implementation • 22 Sep 2020 • Ricardo Baptista, Youssef Marzouk, Olivier Zahm
Transportation of measure provides a versatile approach for modeling complex probability distributions, with applications in density estimation, Bayesian inference, generative modeling, and beyond.
1 code implementation • 11 Jun 2020 • Ricardo Baptista, Bamdad Hosseini, Nikola B. Kovachki, Youssef Marzouk
We present a novel framework for conditional sampling of probability measures, using block triangular transport maps.
no code implementations • 30 Jun 2019 • Alessio Spantini, Ricardo Baptista, Youssef Marzouk
We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time.
2 code implementations • ICML 2018 • Ricardo Baptista, Matthias Poloczek
The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences.
no code implementations • NeurIPS 2017 • Rebecca E. Morrison, Ricardo Baptista, Youssef Marzouk
We present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data.